code: def predict(
      self, base_models_predictions: Union[np.ndarray, List[np.ndarray]]
  ) -> np.ndarray:
      average = np.zeros_like(
          base_models_predictions[0], dtype=np.float64
      )
      tmp_predictions = np.empty_like(
          base_models_predictions[0], dtype=np.float64
      )
  
      # if predictions.shape[0] == len(self.weights_),
      # predictions include those of zero-weight models.
      if len(base_models_predictions) == len(self.weights_):
          for pred, weight in zip(
              base_models_predictions, self.weights_
          ):
              np.multiply(pred, weight, out=tmp_predictions)
              np.add(average, tmp_predictions, out=average)
  
      # if prediction model.shape[0] == len(non_null_weights),
      # predictions do not include those of zero-weight models.
      elif len(base_models_predictions) == np.count_nonzero(
          self.weights_
      ):
          non_null_weights = [w for w in self.weights_ if w > 0]
          for pred, weight in zip(
              base_models_predictions, non_null_weights
          ):
              np.multiply(pred, weight, out=tmp_predictions)
              np.add(average, tmp_predictions, out=average)
  
      # If none of the above applies, then something must have gone wrong.
      else:
          raise ValueError(
              "The dimensions of ensemble predictions"
              " and ensemble weights do not match!"
          )
      del tmp_predictions
      return average
  
imports: from __future__ import annotations
  from typing import Dict, List, Sequence, Tuple, Union
  import random
  import warnings
  from collections import Counter
  import numpy
  from sklearn.utils import check_random_state
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import TASK_TYPES
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
label_desc: Use image node_id 8 for calling the EnsembleSelection obj's underlying member method code with example usage: obj.predict(base_models_predictions) and returns: average